AI and Data Science are all the rage, but there is a problem that no one talks about. […] Deploying and maintaining [AI systems] over time is getting exponentially more complex and expensive.

Designing and building a data science team is a complex problem; so is determining the nature of interactions between data scientists and the rest of the organization.

If we allow this incredible technology to continue to advance but fail to address questions around biases, our society will undoubtedly face a variety of serious moral, legal, practical and social consequences. It’s important we act now to mitigate the spread of biased or inaccurate technologies.

Impostor syndrome is the elephant in the data science lab. Everyone has it, no one thinks other people have it, and no one talks about it.

The industry demand for Data Engineers is constantly on the rise, and with it more and more software engineers and recent graduates try to enter the field. The biggest hurdle for newcomers lies in understanding the Data Engineering landscape and getting hands-on experience with relevant frameworks.